When I run the example/image-classification/train_mnist.py, I was told that TypeError: fit() got an unexpected keyword argument 'monitor'. I have change nothing in this file. And I have just download the latest version from the github.
example\image-classification>python train_mnist.py
INFO:root:start with arguments Namespace(batch_size=64, disp_batches=100, gpus=N
one, kv_store='device', load_epoch=None, lr=0.05, lr_factor=0.1, lr_step_epochs=
'10', model_prefix=None, mom=0.9, monitor=0, network='mlp', num_classes=10, num_
epochs=20, num_examples=60000, num_layers=None, optimizer='sgd', test_io=0, top_
k=0, wd=0.0001)
Traceback (most recent call last):
File "train_mnist.py", line 76, in <module>
fit.fit(args, sym, get_mnist_iter)
File "D:\Zhenxingjian\MxNet\mxnet-0.9.3\mxnet-0.9.3\example\image-classificati
on\common\fit.py", line 183, in fit
monitor = monitor)
TypeError: fit() got an unexpected keyword argument 'monitor'
I was able to successfully run train_mnist.py example on v09.3, v0.9.5 and v0.10.0. If the problem still persists for you, maybe checkout the latest version and try again.
Related
I run the following line at my terminal and I got an error, but it works fine in jupyter notebook.
df._repr_html_()
Traceback (most recent call last):
File "", line 1, in
File "/usr/lib/spark/python/pyspark/sql/dataframe.py", line 1020, in getattr
"'%s' object has no attribute '%s'" % (self.class.name, name))
AttributeError: 'DataFrame' object has no attribute '_repr_html_'
It doesn't work on my terminal because it's not a visual interpreter, so I had to go through the data frame collecting the values I needed and creating a string like html. Then, I could attached to an email I send from python at the terminal and works fine.
I want to convert trained python model (.pb) to tensorflowjs model. To accomplish this, first I saved the model with estimator.export_savedmodel function, then I run the tensorflowjs_converter command on Google Colab. However, no file is created for tensorflowjs. The conversion also gives a lot of warning and ends with an error.
Here is the full code, and please run to see the full output:
https://colab.research.google.com/drive/19k2s8eHpQY9Trps9dyaxPp0HqHWp5qpb
What is the reason of the problem and how can I fix it?
Part of the output:
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
Traceback (most recent call last):
File "/usr/local/bin/tensorflowjs_converter", line 8, in <module>
sys.exit(pip_main())
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/converter.py", line 638, in pip_main
main([' '.join(sys.argv[1:])])
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/converter.py", line 642, in main
convert(argv[0].split(' '))
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/converter.py", line 591, in convert
strip_debug_ops=args.strip_debug_ops)
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py", line 435, in convert_tf_saved_model
strip_debug_ops=strip_debug_ops)
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py", line 141, in optimize_graph
', '.join(unsupported))
ValueError: Unsupported Ops in the model before optimization
ParallelDynamicStitch, StringSplit, Unique, RegexReplace, DynamicPartition, StringToHashBucketFast, ParseExample, LookupTableFindV2, LookupTableSizeV2, SparseFillEmptyRows, StringJoin, AsString, SparseSegmentSqrtN, HashTableV2
Edit:
Seems like it isn't supported:
https://github.com/tensorflow/tfjs/issues/2322
This is because your model has ops that are not supported by tensorflow.js yet. And seems like you missed the missing op name in the output you pasted. Please feel free to update the output with missing op name or file a feature request in the tensorflow.js repo with more details.
Trying to run GitHub codes of nonlocal recurrent networks.
I am ending up getting this error. How to debug this error?
Traceback (most recent call last):
File "trainer.py", line 97, in
tf.logging.dict[hparams.verbosity] / 10)
KeyError: 'INFO'
tried editing codes. but not working.
On tensorflow version 1.14, below code will cause the error.
tf.logging.dict[hparams.verbosity]
So you can fix the code like below. It will be ok on tf 1.14.
getattr(tf.logging, hparams.verbosity)
When running a Tensorflow implementation, I got the following error/warning messages, which does not include the line of python code that causes this issue. At the same time, the result is still generated. I am not sure what do these messages indicate?
Exception ignored in: <bound method Session.__del__ of <tensorflow.python.client.session.Session object at 0x2b48ec89f748>>
Traceback (most recent call last):
File "/data/tfw/lib/python3.4/site- packages/tensorflow/python/client/session.py", line 140, in __del__
File "/data/tfw/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 137, in close
UnboundLocalError: local variable 'status' referenced before assignment
Today I also encountered this exception while running some Multi layer perceptron model on Windows 10 64 Bit with Python 3.5 and TensorFlow 0.12
I have seen this answer for this exception
it induced by different gc sequence, if python collect session first , the program will exit successfully, if python collect swig memory(tf_session) first, the program exit with failure.
Here
I want to view statistics of my model by saving my graph to a file then running graph_metrics.py.
I have tried a few different things to write the file, my best effort is:
tf.train.write_graph( session.graph_def, ".", "my_graph", as_text=True )
But here's what happens:
$ python ./util/graph_metrics.py --noinput_binary --graph my_graph
Traceback (most recent call last):
File "./util/graph_metrics.py", line 137, in <module>
tf.app.run()
File ".virtualenv/local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 30, in run
sys.exit(main(sys.argv))
File "./util/graph_metrics.py", line 85, in main
FLAGS.batch_size)
File "./util/graph_metrics.py", line 109, in calculate_graph_metrics
input_tensor = sess.graph.get_tensor_by_name(input_layer)
File ".virtualenv/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2531, in get_tensor_by_name
return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
File ".virtualenv/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2385, in as_graph_element
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
File ".virtualenv/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2427, in _as_graph_element_locked
"graph." % (repr(name), repr(op_name)))
KeyError: "The name 'Mul:0' refers to a Tensor which does not exist. The operation, 'Mul', does not exist in the graph."
Is there a complete working example of saving a graph, then analyzing it with graph_metrics.py?
This process seems to involve a magic incantation that I haven't yet discovered.
The error you're hitting is because you need to specify the name of your own input node with --input_layer= (it just defaults to Mul:0 because that's what we use in one of our Inception models):
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/graph_metrics.py#L51
The graph_metrics script is still very much a work in progress unfortunately, and you may hit problems with shape inference, but hopefully this should get you past the initial hurdle.